Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Entire DC Network
Organ Segmentation Of Pediatric Computed Tomography (Ct) With Generative Adversarial Networks, Chi Nok Enoch Kan
Organ Segmentation Of Pediatric Computed Tomography (Ct) With Generative Adversarial Networks, Chi Nok Enoch Kan
Master's Theses (2009 -)
Accurately segmenting organs in abdominal computed tomography (CT) is crucial for many clinical applications such as organ-specific dose estimation. With the recent emergence of deep learning techniques for computer vision, many powerful frameworks are proposed for organ segmentation in abdominal CT images. A major problem with these state-of-the-art methods is that they depend on large amounts of training data to achieve high segmentation accuracy. Pediatric abdominal CTs are particularly hard to obtain since these children are much more sensitive to ionizing radiation than adults. It is extremely challenging to train automatic segmentation algorithms on pediatric CT volumes. To address these …
Deep Learning For Quantitative Susceptibility Mapping Reconstruction, Juan Liu
Deep Learning For Quantitative Susceptibility Mapping Reconstruction, Juan Liu
Dissertations (1934 -)
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that estimates tissue magnetic susceptibility from Larmor frequency offset measurements. The generation of QSM requires solving ill-posed background field removal (BFR) and field-to-source inversion problems. Incorrect BFR often introduces erroneous local field outputs and subsequently affects susceptibility quantification accuracy. Inaccurate field-to-source inversion often causes large susceptibility estimation errors that appear as streaking artifacts in the QSM, especially in massive hemorrhagic regions. Because current QSM techniques struggle to generate reliable QSM, the clinical translation of QSM is greatly hindered. Recently, deep learning (DL) has achieved state-of-the-art performance in many computer …